Feature subset selection can improve software cost estimation accuracy
Zhihao Chen, Tim Menzies, Dan Port, BARRY W. BOEHM · ACM SIGSOFT Software Engineering Notes · 2005
Cost estimation is important in software development for controlling and planning software risks and schedule. Good estimation models, such as COCOMO, can avoid insufficient resources being allocated to a project. In this study, we find that COCOMO's estimates can be improved via WRAPPER- a feature subset selection method developed by the data mining community. Using data sets from the PROMISE repository, we show WRAPPER significantly and dramatically improves COCOMO's predictive power.